AI is redefining decision-making in biopharma R&D
AI has kickstarted a new wave of innovation in biopharma R&D, reshaping how organisations approach some of the most complex and high-stakes decision making in science and medicine.
As advances in computing power, data availability, and generative AI converge, the medical industry has the opportunity to make more informed choices earlier in the drug development process.
This shift marks an important evolution in how the industry approaches innovation. One that could help organisations bring more effective therapies to patients, faster and with greater confidence.
Redirecting focus to where decisions matter most
The biopharma industry faces unique challenges when it comes to R&D. Firms are focused on creating new, effective, and safe drugs while adhering to complex development timelines, clinical cycles, and regulatory pathways. Because of this, the core value add of technology adoption has historically been considered with acceleration in mind.
But, while speed will always remain important, the greatest opportunity lies far earlier in the R&D lifecycle. The most consequential decisions in drug development are rarely made at the point of clinical execution. They happen much earlier, in target identification, molecule selection, and portfolio prioritisation. These early-stage choices play a defining role in determining downstream success and reduce overall time-to-market.
The biggest opportunity may not be to move faster through existing processes, but to make better decisions before organisations commit years of investment and development effort.
How AI is enhancing early-stage discovery
Thanks to recent advancements, AI is increasingly helping organisations improve the quality of candidates entering the pipeline. By identifying weak targets and compounds earlier, organisations can reduce the likelihood of investing heavily in programmes that ultimately fail in later-stage development.
Improvements in computing power and modelling techniques are also extending AI's role across early-stage discovery. Beyond target identification, AI can predict protein folding and binding affinities, generate and rank potential targets before wet-lab work begins, and help researchers prioritise the most promising experiments to pursue.
This is now becoming visible in drug pipelines. According to our ‘Gen AI in Life Sciences’ report, for example, over four in 10 organisations have implemented AI for target identification in drug delivery. Within the next 10 years, almost two thirds of industry leaders anticipate that AI-driven platforms could be responsible for identifying the majority of new molecular entities.
The emergence of AI in early-stage discovery introduces a more profound change than automation alone. It begins to reshape portfolio risk, capital allocation, and scientific governance. In effect, it moves AI from being a tool that supports research to a system that helps steer it.
Building trusted AI foundations across the value chain
As AI takes on a more central role in R&D, establishing trust becomes increasingly essential. Organisations are increasingly focused on building robust frameworks that ensure AI-generated insights are transparent, explainable, and grounded in scientific understanding.
In order to assist biopharma R&D, AI agents must be able to challenge hypotheses and justify conclusions in ways researchers can interrogate – black-box, probabilistic answers are not enough. By deploying specialised models that are trained on domain-specific scientific data and grounded in molecular and biological principles, firms have the opportunity to treat AI agents as trusted companions.
It’s a time of great opportunity, and it’ll be a defining one for many biopharma firms.
Unlocking value through data readiness
Data is central to realising the full potential of AI in biopharma. While the industry has access to vast volumes of scientific and clinical data, unlocking its value requires integration, standardisation, and readiness for AI-driven analysis.
Encouragingly, organisations are making progress. Many are investing in modern data platforms and forming ecosystem partnerships to connect datasets, infrastructure, and expertise. Initiatives such as UK Biobank, the National Center for Biotechnology Information, and The Cancer Genome Atlas are helping to expand access to high-quality, linked data resources.
Looking ahead, synthetic data is also set to play a growing role. By augmenting clinical datasets and modelling patient outcomes, it can help generate richer evidence while reducing reliance on scarce real-world data, particularly in rare diseases. Over time, this has the potential to shorten development timelines and accelerate access to therapies.
Redefining productivity in the age of AI
The next era of productivity will see AI evolve beyond efficiency and discovery towards improved decision quality across the R&D lifecycle. Target identification is currently the most widely adopted AI use case, with 43% of organisations in our research implementing it and reporting an average 28% time savings, for instance. But adoption will continue to expand across value chain. Organisations who are ready to scale AI responsibly as a cross-functional capability that connects science, operations, and decision-making will come out on top.
Ultimately, this is about building a more intelligent, adaptive, and resilient model of R&D – one capable of improving both the speed and quality of decisions at scale.
About the author
Rob Pears is head of life sciences, automotive, and manufacturing at Capgemini in the UK. He works with organisations across highly regulated industries to harness data, AI, and digital technologies that accelerate innovation, improve operational performance, and support the safe, secure delivery of business transformation. With deep expertise across these sectors, he helps shape strategies that deliver sustainable growth, operational excellence, and measurable business value.
